Lidar and camera synchronization

ABSTRACT

A method and system for synchronizing a lidar and a camera on an autonomous vehicle. The system instructs the camera to detect light columns transmitted by the lidar. The system iterates through various start times for the camera. The system instructs the lidar to emit a plurality of light columns at a lidar frequency. The system instructs the camera to capture images at a camera frequency starting at each start time. The system analyzes the image data received from the cameras to identify light columns captured in the images. The system calculates an alignment score for each of the many start times based on the identified light columns. The start time with the optimal alignment score is selected and used to synchronize the lidar and the camera. With lidar data detected by the synchronized lidar and image data captured by the synchronized camera, the system may navigate the autonomous vehicle.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a divisional application of co-pending U.S.application Ser. No. 16/163,463 filed on Oct. 17, 2018, which claims thebenefit and priority of U.S. Provisional Application No. 62/574,744filed Oct. 19, 2017, both of which are incorporated by reference hereinin their entirety.

BACKGROUND Field of the Art

This disclosure generally relates to synchronization of sensors ofvehicles, for example, autonomous vehicles, and more particularly tosynchronization of lidars and cameras installed on a vehicle for use ingeneration of high definition maps.

Prior Art

Autonomous vehicles, also known as self-driving cars, driverless cars,auto, or robotic cars, drive from a source location to a destinationlocation without requiring a human driver to control and navigate thevehicle. Automation of driving is difficult due to several reasons. Forexample, autonomous vehicles use sensors to make driving decisions onthe fly, but vehicle sensors cannot observe everything all the time.Vehicle sensors can be obscured by corners, rolling hills, and othervehicles. Vehicles sensors may not observe certain things early enoughto make decisions. In addition, lanes and signs may be missing on theroad or knocked over or hidden by bushes, and therefore not detectableby sensors. Furthermore, road signs for rights of way may not be readilyvisible for determining from where vehicles could be coming, or forswerving or moving out of a lane in an emergency or when there is astopped obstacle that must be passed.

Autonomous vehicles can use map data to figure out some of the aboveinformation instead of relying on sensor data. However conventional mapshave several drawbacks that make them difficult to use for an autonomousvehicle. For example maps do not provide the level of accuracy requiredfor safe navigation (e.g., 10 cm or less). GPS systems provideaccuracies of approximately 3-5 meters, but have large error conditionsresulting in an accuracy of over 100 m. This makes it challenging toaccurately determine the location of the vehicle. In particular, varioussensors used by the autonomous vehicles for navigation have varyingparameters that can introduce discrepancies between data collected fromthe different sensors.

Autonomous vehicles use various different sensors for self-driving, forexample, lidar and camera sensors. Each sensor of the autonomousvehicle, may use its own coordinate system. Different sensors may recorddata at their own pace, for example, a VELODYNE light detection andranging sensor (lidar or lidar) may collect scans at 10 Hz, whilecameras may collect frames at 30 Hz, and the GPS/IMU may collect data at100 Hz. For example, the lidar may use one coordinate system and acamera may use another coordinate system. Data from different sensorsare timestamped by different clocks. For example, GPS uses satellitetime, VELODYNE lidar uses an internal clock but can also take externaltriggering signals, and many cameras do not have internal clock and usesystem clock. As a result, there can be problems in processing sensordata. The clocks used by different sensors usually do not agree witheach other. For example, the system clock can drift over time while thesatellite time is accurate. Also, some sensor data is not timestamped attime of capture, but is timestamped at the time of delivery. Forexample, camera frames may not be timestamped when the pixels are halfway through exposure, but when the full image has been delivered tosystem memory buffer. Depending on how data is buffered and transferred,there can be a significant delay. Poor synchronization creates problemswhen the HD map system combines data from multiple sources, for eithermap creation or localization. E.g., the HD map system could end upcoloring a point cloud with camera images captured 10 millisecondsearlier, thus many high definition map cells may get assigned wrongcolor. False color has further effect on downstream processes such asfeature labeling, for example, labeled lane lines may shift from theirtrue location due to false color in high definition map cells.

SUMMARY

Navigational systems for vehicles depend on accurate and precise sensordata. The sensor data received from various sensors on a vehicle neednot only be accurate and precise on their own regards but also need tobe precise relative to one another. In particular, utilization of lidarscans in tandem with camera images relies on some degree of precision ininternal clocks within the lidar and the camera image. Embodiments ofthis invention allow synchronization of a lidar and a camera on anautonomous vehicle with a high degree of precision, e.g., less than 5milliseconds.

Embodiments synchronize a lidar and a camera on an autonomous vehicleusing analysis of track samples recorded by the lidar and the camera.The system selects a plurality of track samples for a route including alidar scan and an image. For each track sample, the system calculates atime shift by iterating many time deltas. For each time delta, thesystem adjusts a camera timestamp by that time delta, projecting a lidarscan onto the image as a lidar projection according to the adjustedcamera timestamp, and calculating an alignment score of the lidarprojection for that time delta. The system defines the time shift foreach track sample as the time delta with the highest alignment score.The system models time drift of the camera compared to the lidar basedon the calculated time shifts for the track samples and synchronizes thelidar and the camera according to the modeled time drift.

Another embodiment synchronizes a lidar and a camera by instructing thecamera to detect light columns transmitted by the lidar onto areflective surface. The system iterates through various start times forthe camera. The system instructs the lidar to emit a plurality of lightcolumns against the reflective surface at a lidar frequency. The systemthen instructs the camera to capture images at a camera frequencystarting at each start time. The system analyzes the image data receivedfrom the cameras to identify light columns captured in the images. Thesystem then calculates an alignment score for each of the many starttimes based on the identified light columns. The start time with theoptimal alignment score is selected and used to synchronize the lidarand the camera.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

FIG. 1 illustrates an overall system environment of an HD map systeminteracting with multiple vehicle computing systems, according to anembodiment.

FIG. 2 illustrates the system architecture of a vehicle computingsystem, according to an embodiment.

FIG. 3 illustrates the module architecture of a perception module,according to an embodiment.

FIG. 4 illustrates a flowchart describing a process of projecting lidardata onto an image for use by the navigational system, according to anembodiment.

FIG. 5 illustrates a flowchart describing a first method forsynchronizing a lidar and a camera on a vehicle, according to anembodiment.

FIG. 6 demonstrates the first method for synchronizing the lidar and thecamera, according to an embodiment.

FIG. 7 illustrates a flowchart describing a second method forsynchronizing a lidar and a camera on a vehicle, according to anembodiment.

FIG. 8A illustrates a placement configuration of the lidar and thecamera for the second method for synchronizing the lidar and the camera,according to an embodiment.

FIGS. 8B & 8C demonstrates the second method for synchronizing the lidarand the camera, according to an embodiment.

FIG. 9 illustrates the various layers of instructions in the HD Map APIof a vehicle computing system, according to an embodiment.

FIG. 10 illustrates the system architecture of an HD map system,according to an embodiment.

FIG. 11 illustrates the components of an HD map, according to anembodiment.

FIGS. 12A & 12B illustrate geographical regions defined in an HD map,according to an embodiment.

FIG. 13 illustrates representations of lanes in an HD map, according toan embodiment.

FIGS. 14A & 14B illustrate lane elements and relations between laneelements in an HD map, according to an embodiment.

FIG. 15 illustrates an embodiment of a computing machine that can readinstructions from a machine-readable medium and execute the instructionsin a processor or controller.

The figures depict various embodiments of the present invention forpurposes of illustration only. One skilled in the art will readilyrecognize from the following discussion that alternative embodiments ofthe structures and methods illustrated herein may be employed withoutdeparting from the principles of the invention described herein.

DETAILED DESCRIPTION

Navigational systems for vehicles depend on accurate and precise sensordata. The sensor data received from various sensors on a vehicle neednot only be accurate and precise on their own regards but also need tobe precise relative to one another. In particular, utilization of lidarscans in tandem with camera images rely on some degree of precision ininternal clocks within the lidar and the camera image. Embodiments ofthis invention allow synchronization of a lidar and a camera on anautonomous vehicle with a high degree of precision, e.g., less than 5milliseconds. In an embodiment, the synchronization is performed inreal-time while the vehicle is being driven. Furthermore, thesynchronization is performed automatically and the vehicle does not haveto be taken to an expert for performing synchronization.

Navigational System Environment

FIG. 1 shows an overall navigational system environment 100 of an HD mapsystem 110 interacting with multiple vehicles 150, according to anembodiment. The navigational system environment 100 includes an HD mapsystem 110 that interacts with a plurality of vehicles 150. The vehicles150 may be autonomous vehicles but are not required to be. The HD mapsystem 110 receives sensor data captured by sensors of the vehicles, andcombines the data received from the vehicles 150 to generate andmaintain HD maps. The HD map system 110 sends HD map data to thevehicles for use in driving the vehicles. In an embodiment, the HD mapsystem 110 is implemented as a distributed computing system, forexample, a cloud based service that allows clients such as vehiclecomputing systems 120 to make requests for information and services. Forexample, a vehicle computing system 120 may make a request for HD mapdata for driving along a route and the HD map system 110 provides therequested HD map data.

FIG. 1 and the other figures use like reference numerals to identifylike elements. A letter after a reference numeral, such as “105A,”indicates that the text refers specifically to the element having thatparticular reference numeral. A reference numeral in the text without afollowing letter, such as “105,” refers to any or all of the elements inthe figures bearing that reference numeral (e.g. “105” in the textrefers to reference numerals “105A” and/or “105N” in the figures).

The HD map system 110 comprises a vehicle interface module 160 and an HDmap store 165. The HD map system 110 interacts with the vehiclecomputing system 120 of various vehicles 150 using the vehicle interfacemodule 160. The HD map system 110 stores map information for variousgeographical regions in the HD map store 165. The HD map system 110 mayinclude other modules than those shown in FIG. 1, for example, variousother modules as illustrated in FIG. 10 and further described herein.

The HD map system 110 receives 115 data collected by sensors of aplurality of vehicles 150, for example, hundreds or thousands of cars.The vehicles provide sensor data captured while driving along variousroutes and send it to the HD map system 110. The HD map system 110 usesthe data received from the vehicles 150 to create and update HD mapsdescribing the regions in which the vehicles 150 are driving. The HD mapsystem 110 builds high definition maps based on the collectiveinformation received from the vehicles 150 and stores the HD mapinformation in the HD map store 165.

The HD map system 110 sends 125 HD maps to individual vehicles 150 asrequired by the vehicles 150. For example, if an autonomous vehicleneeds to drive along a route, the vehicle computing system 120 of theautonomous vehicle provides information describing the route beingtraveled to the HD map system 110. In response, the HD map system 110provides the required HD maps for driving along the route.

In an embodiment, the HD map system 110 sends portions of the HD mapdata to the vehicles in a compressed format so that the data transmittedconsumes less bandwidth. The HD map system 110 receives from variousvehicles, information describing the data that is stored at the local HDmap store 275 of the vehicle. If the HD map system 110 determines thatthe vehicle does not have certain portion of the HD map stored locallyin the local HD map store 275, the HD map system 110 sends that portionof the HD map to the vehicle. If the HD map system 110 determines thatthe vehicle did previously receive that particular portion of the HD mapbut the corresponding data was updated by the HD map system 110 sincethe vehicle last received the data, the HD map system 110 sends anupdate for that portion of the HD map stored at the vehicle. This allowsthe HD map system 110 to minimize the amount of data that iscommunicated with the vehicle and also to keep the HD map data storedlocally in the vehicle updated on a regular basis.

A vehicle 150 includes vehicle sensors 105, vehicle controls 130, and avehicle computing system 120. The vehicle sensors 105 allow the vehicle150 to detect the surroundings of the vehicle as well as informationdescribing the current state of the vehicle, for example, informationdescribing the location and motion parameters of the vehicle. Thevehicle sensors 105 comprise at least a camera, a light detection andranging sensor (lidar), a global positioning system (GPS) navigationsystem, and an inertial measurement unit (IMU), among other varioussensors. The vehicle has one or more cameras that capture images of thesurroundings of the vehicle. A lidar surveys the surroundings of thevehicle by measuring distance to a target by illuminating that targetwith a laser light pulses, and measuring the reflected pulses. The lidarmay emit laser light pulses along a vertical light column which may berevolved in 360°. When revolving in 360°, each laser light pulse in thevertical light column creates a lidar scan line. The detected pulsesreflected off objects in the surrounding area are recorded as lidarpoint clouds. In one instance of scanning by the lidar, the lidarrecords the detected lidar point clouds as a lidar scan. Each detectedpulse is used by the lidar to calculate a distance traveled by thepulse. The lidar may additionally record a reflective intensity of eachdetected pulse, wherein various objects or surfaces have varyingreflective properties—e.g., a mirror has a different reflectancecompared to a piece of cloth. The GPS navigation system determines theposition of the vehicle based on signals from satellites. An IMU is anelectronic device that measures and reports motion data of the vehiclesuch as velocity, acceleration, and direction of movement, speed,angular rate, and so on using a combination of accelerometers andgyroscopes or other measuring instruments.

The vehicle controls 130 control the physical movement of the vehicle,for example, acceleration, direction change, starting, stopping, and soon. The vehicle controls 130 include the machinery for controlling theaccelerator, brakes, steering wheel, and so on. The vehicle computingsystem 120 continuously provides control signals to the vehicle controls130, thereby causing an autonomous vehicle to drive along a selectedroute.

The vehicle computing system 120 performs various tasks includingprocessing data collected by the sensors as well as map data receivedfrom the HD map system 110. The vehicle computing system 120 alsoprocesses data for sending to the HD map system 110. Details of thevehicle computing system are illustrated in FIG. 2 and further describedin connection with FIG. 2.

The interactions between the vehicle computing systems 120 and the HDmap system 110 are typically performed via a network, for example, viathe Internet. The network enables communications between the vehiclecomputing systems 120 and the HD map system 110. In one embodiment, thenetwork uses standard communications technologies and/or protocols. Thedata exchanged over the network can be represented using technologiesand/or formats including the hypertext markup language (HTML), theextensible markup language (XML), etc. In addition, all or some of linkscan be encrypted using conventional encryption technologies such assecure sockets layer (SSL), transport layer security (TLS), virtualprivate networks (VPNs), Internet Protocol security (IPsec), etc. Inanother embodiment, the entities can use custom and/or dedicated datacommunications technologies instead of, or in addition to, the onesdescribed above.

Computing Machine Architecture

Reference is now made to FIG. 15 which presents an example computingsystem; the structure, the operations, and the functions thereof may beimplemented in any of the computing systems discussed herein thisdisclosure.

FIG. 15 is a block diagram illustrating components of an examplecomputing system able to read instructions from a machine-readablemedium and execute them in a processor (or controller). Specifically,FIG. 15 shows a diagrammatic representation of a machine in the exampleform of a computing system 1500 within which instructions 1524 (e.g.,software) for causing the machine to perform any one or more of themethodologies discussed herein may be executed. In alternativeembodiments, the machine operates as a standalone device or may beconnected (e.g., networked) to other machines. In a networkeddeployment, the machine may operate in the capacity of a server machineor a client machine in a server-client network environment, or as a peermachine in a peer-to-peer (or distributed) network environment.

The computing system may be a server computer, a client computer, apersonal computer (PC), a tablet PC, a set-top box (STB), a personaldigital assistant (PDA), a cellular telephone, a smartphone, a webappliance, a network router, switch or bridge, or any machine capable ofexecuting instructions 1524 (sequential or otherwise) that specifyactions to be taken by that machine. Further, while only a singlemachine is illustrated, the term “machine” shall also be taken toinclude any collection of machines that individually or jointly executeinstructions 1524 to perform any one or more of the methodologiesdiscussed herein.

The example computing system 1500 includes a processor 1502 (e.g., acentral processing unit (CPU), a graphics processing unit (GPU), adigital signal processor (DSP), one or more application specificintegrated circuits (ASICs), one or more radio-frequency integratedcircuits (RFICs), or any combination of these), a main memory 1504, anda static memory 1506, which are configured to communicate with eachother via a bus 1508. The computing system 1500 may further includegraphics display unit 1510 (e.g., a plasma display panel (PDP), a liquidcrystal display (LCD), a projector, or a cathode ray tube (CRT)). Thecomputing system 1500 may also include alphanumeric input device 1512(e.g., a keyboard), a cursor control device 1514 (e.g., a mouse, atrackball, a joystick, a motion sensor, or other pointing instrument), astorage unit 1516, a signal generation device 1518 (e.g., a speaker),and a network interface device 1520, which also are configured tocommunicate via the bus 1508.

The storage unit 1516 includes a machine-readable medium 1522 on whichis stored instructions 1524 (e.g., software) embodying any one or moreof the methodologies or functions described herein. The instructions1524 (e.g., software) may also reside, completely or at least partially,within the main memory 1504 or within the processor 1502 (e.g., within aprocessor's cache memory) during execution thereof by the computingsystem 1500, the main memory 1504 and the processor 1502 alsoconstituting machine-readable media. The instructions 1524 (e.g.,software) may be transmitted or received over a network 1526 via thenetwork interface device 1520.

While machine-readable medium 1522 is shown in an example embodiment tobe a single medium, the term “machine-readable medium” should be takento include a single medium or multiple media (e.g., a centralized ordistributed database, or associated caches and servers) able to storeinstructions (e.g., instructions 1524). The term “machine-readablemedium” shall also be taken to include any medium that is capable ofstoring instructions (e.g., instructions 1524) for execution by themachine and that cause the machine to perform any one or more of themethodologies disclosed herein. The term “machine-readable medium”includes, but not be limited to, data repositories in the form ofsolid-state memories, optical media, and magnetic media.

Vehicle Computing System Architecture

FIG. 2 shows the system architecture of a vehicle computing system 120,according to an embodiment. The vehicle computing system 120 mayimplement the structure, operations, and functions of the examplecomputing system 1500 described above. The vehicle computing system 120comprises a perception module 210, prediction module 215, planningmodule 220, a control module 225, a local HD map store 275, an HD mapsystem interface 280, and an HD map application programming interface(API) 205. The various modules of the vehicle computing system 120process various type of data including sensor data 230, a behavior model235, routes 240, and physical constraints 245. In other embodiments, thevehicle computing system 120 may have more or fewer modules.Functionality described as being implemented by a particular module maybe implemented by other modules.

The perception module 210 receives sensor data 230 from the sensors 105of the vehicle 150. This includes image data (e.g., images and/or video)collected by one or more cameras on the vehicle 150, lidar data capturedby a lidar on the vehicle 150, IMU data collected by an IMU on thevehicle 150, GPS data collected by a GPS navigation system, and so on.In some embodiments, the sensor data 230 may be collated into tracksamples along a route, with each track sample including lidar datacaptured at a lidar timestamp, one or more images captured at a cameratimestamp. The perception module 210 uses the sensor data to determinewhat objects are around the vehicle, the details of the road on whichthe vehicle is travelling, and so on. The perception module 210processes the sensor data 230 to populate data structures storing thesensor data and provides the information to the prediction module 215.An embodiment of the perception module 210 is further described below inreference to FIG. 3.

The prediction module 215 interprets the data provided by the perceptionmodule using behavior models of the objects perceived to determinewhether an object is moving or likely to move. For example, theprediction module 215 may determine that objects representing road signsare not likely to move, whereas objects identified as vehicles, people,and so on, are either moving or likely to move. The prediction module215 uses the behavior models 235 of various types of objects todetermine whether they are likely to move. The prediction module 215provides the predictions of various objects to the planning module 200to plan the subsequent actions that the vehicle needs to take next.

The planning module 200 receives the information describing thesurroundings of the vehicle from the prediction module 215, the route240 that determines the destination of the vehicle, and the path thatthe vehicle should take to get to the destination. The planning module200 uses the information from the prediction module 215 and the route240 to plan a sequence of actions that the vehicle needs to take withina short time interval, for example, within the next few seconds. In anembodiment, the planning module 200 specifies the sequence of actions asone or more points representing nearby locations that the vehicle needsto drive through next.

The planning module 200 provides the details of the plan comprising thesequence of actions to be taken by the vehicle to the control module225. The plan may determine the subsequent action of the vehicle, forexample, whether the vehicle performs a lane change, a turn,acceleration by increasing the speed or slowing down, and so on.

The control module 225 determines the control signals for sending to thecontrols 130 of the vehicle based on the plan received from the planningmodule 200. For example, if the vehicle is currently at point A and theplan specifies that the vehicle should next go to a nearby point B, thecontrol module 225 determines the control signals for the controls 130that would cause the vehicle to go from point A to point B in a safe andsmooth way, for example, without taking any sharp turns or a zig zagpath from point A to point B. The path taken by the vehicle to go frompoint A to point B may depend on the current speed and direction of thevehicle as well as the location of point B with respect to point A. Forexample, if the current speed of the vehicle is high, the vehicle maytake a wider turn compared to a vehicle driving slowly.

The control module 225 also receives physical constraints 245 as input.These include the physical capabilities of that specific vehicle. Forexample, a car having a particular make and model may be able to safelymake certain types of vehicle movements such as acceleration, and turnsthat another car with a different make and model may not be able to makesafely. The control module 225 incorporates these physical constraintsin determining the control signals.

The control module 225 sends the control signals to the vehicle controls130 that cause the vehicle to execute the specified sequence of actionscausing the vehicle to move as planned. The above steps are constantlyrepeated every few seconds causing the vehicle to drive safely along theroute that was planned for the vehicle.

The various modules of the vehicle computing system 120 including theperception module 210, prediction module 215, and planning module 220receive map information to perform their respective computation. Thevehicle 150 stores the HD map data in the local HD map store 275. Themodules of the vehicle computing system 120 interact with the map datausing the HD map API 205 that provides a set of application programminginterfaces (APIs) that can be invoked by a module for accessing the mapinformation. The HD map system interface 280 allows the vehiclecomputing system 120 to interact with the HD map system 110 via anetwork (not shown in the Figures). The local HD map store 275 storesmap data in a format specified by the HD Map system 110. The HD map API205 is capable of processing the map data format as provided by the HDMap system 110. The HD Map API 205 provides the vehicle computing system120 with an interface for interacting with the HD map data. The HD mapAPI 205 includes several APIs including the localization API 250, thelandmark map API 255, the route API 265, the 3D map API 270, the mapupdate API 285, and so on.

The localization APIs 250 determine the current location of the vehicle,for example, when the vehicle starts and as the vehicle moves along aroute. The localization APIs 250 include a localize API that determinesan accurate location of the vehicle within the HD Map. The vehiclecomputing system 120 can use the location as an accurate relativepositioning for making other queries, for example, feature queries,navigable space queries, and occupancy map queries further describedherein. The localize API receives inputs comprising one or more of,location provided by GPS, vehicle motion data provided by IMU, lidardata, and images. The localize API returns an accurate location of thevehicle as latitude and longitude coordinates. The coordinates returnedby the localize API are more accurate compared to the GPS coordinatesused as input, for example, the output of the localize API may haveprecision range from 5-10 cm. In one embodiment, the vehicle computingsystem 120 invokes the localize API to determine location of the vehicleperiodically based on the lidar data, for example, at a frequency of 10Hz. The vehicle computing system 120 may invoke the localize API todetermine the vehicle location at a higher rate (e.g., 60 Hz) if GPS/IMUdata is available at that rate. The vehicle computing system 120 storesas internal state, location history records to improve accuracy ofsubsequent localize calls. The location history record stores history oflocation from the point-in-time, when the car was turned off/stopped.The localization APIs 250 include a localize-route API generates anaccurate route specifying lanes based on the HD map. The localize-routeAPI takes as input a route from a source to destination via a thirdparty maps and generates a high precision routes represented as aconnected graph of navigable lanes along the input routes based on HDmaps.

The landmark map API 255 provides the geometric and semantic descriptionof the world around the vehicle, for example, description of variousportions of lanes that the vehicle is currently travelling on. Thelandmark map APIs 255 comprise APIs that allow queries based on landmarkmaps, for example, fetch-lanes API and fetch-features API. Thefetch-lanes API provide lane information relative to the vehicle and thefetch-features API. The fetch-lanes API receives as input a location,for example, the location of the vehicle specified using latitude andlongitude of the vehicle and returns lane information relative to theinput location. The fetch-lanes API may specify a distance parametersindicating the distance relative to the input location for which thelane information is retrieved. The fetch-features API receivesinformation identifying one or more lane elements and returns landmarkfeatures relative to the specified lane elements. The landmark featuresinclude, for each landmark, a spatial description that is specific tothe type of landmark.

The 3D map API 265 provides efficient access to the spatial3-dimensional (3D) representation of the road and various physicalobjects around the road as stored in the local HD map store 275. The 3Dmap APIs 965 include a fetch-navigable-surfaces API and afetch-occupancy-grid API. The fetch-navigable-surfaces API receives asinput, identifiers for one or more lane elements and returns navigableboundaries for the specified lane elements. The fetch-occupancy-grid APIreceives a location as input, for example, a latitude and longitude ofthe vehicle, and returns information describing occupancy for thesurface of the road and all objects available in the HD map near thelocation. The information describing occupancy includes a hierarchicalvolumetric grid of all positions considered occupied in the map. Theoccupancy grid includes information at a high resolution near thenavigable areas, for example, at curbs and bumps, and relatively lowresolution in less significant areas, for example, trees and wallsbeyond a curb. The fetch-occupancy-grid API is useful for detectingobstacles and for changing direction if necessary.

The 3D map APIs also include map update APIs, for example,download-map-updates API and upload-map-updates API. Thedownload-map-updates API receives as input a planned route identifierand downloads map updates for data relevant to all planned routes or fora specific planned route. The upload-map-updates API uploads datacollected by the vehicle computing system 120 to the HD map system 110.This allows the HD map system 110 to keep the HD map data stored in theHD map system 110 up to date based on changes in map data observed bysensors of vehicles driving along various routes.

The route API 270 returns route information including full route betweena source and destination and portions of route as the vehicle travelsalong the route. The 3D map API 965 allows querying the HD Map. Theroute APIs 270 include add-planned-routes API and get-planned-route API.The add-planned-routes API provides information describing plannedroutes to the HD map system 110 so that information describing relevantHD maps can be downloaded by the vehicle computing system 120 and keptup to date. The add-planned-routes API receives as input, a routespecified using polylines expressed in terms of latitudes and longitudesand also a time-to-live (TTL) parameter specifying a time period afterwhich the route data can be deleted. Accordingly, the add-planned-routesAPI allows the vehicle to indicate the route the vehicle is planning ontaking in the near future as an autonomous trip. The add-planned-routeAPI aligns the route to the HD map, records the route and its TTL value,and makes sure that the HD map data for the route stored in the vehiclecomputing system 120 is up to date. The get-planned-routes API returns alist of planned routes and provides information describing a routeidentified by a route identifier.

The map update API 285 manages operations related to update of map data,both for the local HD map store 275 and for the HD map store 165 storedin the HD map system 110. Accordingly, modules in the vehicle computingsystem 120 invoke the map update API 285 for downloading data from theHD map system 110 to the vehicle computing system 120 for storing in thelocal HD map store 275 as necessary. The map update API 285 also allowsthe vehicle computing system 120 to determine whether the informationmonitored by the vehicle sensors 105 indicates a discrepancy in the mapinformation provided by the HD map system 110 and uploads data to the HDmap system 110 that may result in the HD map system 110 updating the mapdata stored in the HD map store 165 that is provided to other vehicles150.

FIG. 10 illustrates the various layers of instructions in the HD Map API205 of a vehicle computing system 120, according to an embodiment.Different manufacturer of vehicles have different instructions forreceiving information from vehicle sensors 105 and for controlling thevehicle controls 130. Furthermore, different vendors provide differentcomputer platforms with autonomous driving capabilities, for example,collection and analysis of vehicle sensor data.

Examples of computer platforms for autonomous vehicles include platformsprovided vendors, such as NVIDIA, QUALCOMM, and INTEL. These platformsprovide functionality for use by autonomous vehicle manufacturers inmanufacture of autonomous vehicles. A vehicle manufacturer can use anyone or several computer platforms for autonomous vehicles. The HD mapsystem 110 provides a library for processing HD maps based oninstructions specific to the manufacturer of the vehicle andinstructions specific to a vendor specific platform of the vehicle. Thelibrary provides access to the HD map data and allows the vehicle tointeract with the HD map system 110.

As shown in FIG. 9, in an embodiment, the HD map API is implemented as alibrary that includes a vehicle manufacturer adapter 910, a computerplatform adapter 920, and a common HD map API layer 930. The common HDmap API layer comprises generic instructions that can be used across aplurality of vehicle computer platforms and vehicle manufacturers. Thecomputer platform adapter 920 include instructions that are specific toeach computer platform. For example, the common HD Map API layer 930 mayinvoke the computer platform adapter 920 to receive data from sensorssupported by a specific computer platform. The vehicle manufactureradapter 910 comprises instructions specific to a vehicle manufacturer.For example, the common HD map API layer 930 may invoke functionalityprovided by the vehicle manufacturer adapter 910 to send specificcontrol instructions to the vehicle controls 130.

The HD map system 110 stores computer platform adapters 920 for aplurality of computer platforms and vehicle manufacturer adapters 910for a plurality of vehicle manufacturers. The HD map system 110determines the particular vehicle manufacturer and the particularcomputer platform for a specific autonomous vehicle. The HD map system110 selects the vehicle manufacturer adapter 910 for the particularvehicle manufacturer and the computer platform adapter 920 theparticular computer platform of that specific vehicle. The HD map system110 sends instructions of the selected vehicle manufacturer adapter 910and the selected computer platform adapter 920 to the vehicle computingsystem 120 of that specific autonomous vehicle. The vehicle computingsystem 120 of that specific autonomous vehicle installs the receivedvehicle manufacturer adapter 910 and the computer platform adapter 920.The vehicle computing system 120 periodically checks if the HD mapsystem 110 has an update to the installed vehicle manufacturer adapter910 and the computer platform adapter 920. If a more recent update isavailable compared to the version installed on the vehicle, the vehiclecomputing system 120 requests and receives the latest update andinstalls it.

Perception Module Architecture

FIG. 3 illustrates the module architecture of a perception module 210,according to an embodiment. The perception module 210 uses sensor data230 to contextualize a local area surrounding a vehicle 150. In someembodiments, the sensor data 230 may be collated into track samplesalong a route, with each track sample includes a lidar scan captured ata lidar timestamp and one or more images captured at a camera timestampby one or more cameras, wherein the lidar timestamp and the cameratimestamp are in temporal proximity. The track sample may additionallycontain GPS data and IMU data captured temporally proximal to the lidarscan and/or the images. To contextualize the local area, the perceptionmodule 210 combines lidar data with image data captured by one or morecameras to identify various features in the local area. Features in thelocal area may include one or more objects in the local area, details ofthe road on which the vehicle 150 is travelling, traffic signs along theroad, and so on. The contextualized local area may be used by thevehicle computing system 120 for real-time localization of the vehiclein the HD map or for generating and/or updating the HD map. Theperception module 210 has a pose calculation module 310, a lidarunwinding module 320, a lidar projection module 330, a synchronizationmodule 340, and a data store 350, according to an embodiment. In otherembodiments, the perception module 210 includes additional or fewercomponents that those listed herein. Additionally, various functions ofthe perception module 210 may be variably distributed amongst thecomponents. In alternative embodiments, some or all functions of theperception module 210 may be implemented on components of the HD mapsystem 110.

The pose calculation module 310 determines a pose of the vehicle 150.The pose of the vehicle 150 generally describes a position and anorientation of the vehicle 150 in some reference coordinate system. Inorder to determine a pose for the vehicle 150, the pose calculationmodule 310 uses lidar data from the lidar on the vehicle 150. The posecalculation module 310 uses a pair of lidar scans. With the pair oflidar scans, the pose calculation module 310 implements an iterativeclosest point algorithm (ICP) to initialize a transformation between thepair of lidar scans. The pose calculation module 310 transforms one ofthe lidar scans to approximate the other. The pose calculation module310 calculates an error metric based on the transformed lidar scan andthe other lidar scan that is defined by a distance between correspondingpoint clouds in the transformed lidar scan and the other lidar scan. Thepose calculation module 310 then iteratively adjusts the transformationto minimize the error metric until under a threshold error.

In other embodiments, the pose calculation module 310 determines a poseof the vehicle 150 with image data from one or more cameras on thevehicle 150. In some embodiments, the pose calculation module 310 usesstereoscopic images captured by a pair of stereoscopic cameras on thevehicle 150. The pose calculation module 310 determines a transform fromone of the stereoscopic images to the other. Once determined, the posecalculation module extracts a pose of the vehicle 150 according to thetransform. In other embodiments, the pose calculation module 310determines a pose of the vehicle 150 from video captured by a camera. Inyet other embodiments, the pose calculation module 310 determines a posefrom a single image captured by a camera on the vehicle 150. The posecalculation module 310 knows a position and an orientation of the camerarelative to the vehicle, and based on the image can calculate a pose ofthe vehicle 150.

The lidar unwinding module 320 unwinds a lidar scan. As a single lidarscan is captured over a lidar exposure, the lidar scan may be subject tolidar motion while the vehicle 150 is in motion during the lidarexposure. To recover the true 3D point cloud of the surroundingenvironment relative to the lidar's location at a specific timestamp,the perception module 210 compensates for the lidar's motion during thecourse of scanning the environment. This motion compensation operationis referred to as “unwinding” and the transform applied for unwinding isreferred to as the unwinding transform. The unwinding transform shiftsdifferent lidar point clouds in the lidar scan to account for the lidarmotion. Therefore, embodiments transform the point cloud data using anunwinding transform that compensates for the motion of the lidar andtransforms the lidar scan to one that is consistent with the real world.

In one or more embodiments, the lidar unwinding module 320 determinesthe unwinding transform for the lidar scan. In one embodiment, the lidarunwinding module 320 determines the unwinding transform from consecutivevehicle 150 poses. The lidar unwinding module 320 selects consecutivevehicle 150 poses (e.g., determined by the pose calculation module 310)that temporally bound the lidar exposure for the lidar scan. The lidarunwinding module 320 calculates a total transform between the twoconsecutive poses. The lidar unwinding module 320 then determines atemporal position of the lidar timestamp and relative to the consecutiveposes. Based on the temporal position, the lidar exposure, and the totaltransform, the lidar unwinding module 320 calculates the unwindingtransform for use in unwinding the lidar scan.

The lidar projection module 330 projects a lidar scan onto one or moreimages. The lidar projection module 330 retrieves the one or more imagesin the track sample with the lidar scan (e.g., unwound by the lidarunwinding module 320). The lidar scan is recorded by the lidar to havebeen captured at the lidar timestamp, while the images are recorded bythe camera to have been captured at the camera timestamp. The lidarprojection module 330 according to a known lidar-Camera transform (e.g.,provided by as input or from an external system) converts each pointcloud in the lidar scan into image coordinates in the images accordingto the camera timestamps. Once the lidar projection module 330determines a corresponding image coordinate to map each lidar pointcloud, the lidar projection module 330 generates a lidar projection,wherein the lidar scan is mapped onto the image coordinates. In someembodiments, the lidar-Camera transform also adjusts for rolling shuttercorrection of the images. In a similar manner to that described abovewith the lidar motion, the images may be captured over a camera exposurethat creates distortions in moving objects when in the camera is in theprocess of capturing an image by raster scanning a photodiode array. Tocompensate for the rolling shutter, the lidar-camera transform mayinclude rolling shutter correction. In some embodiments with multipleimages, the lidar projection module 330 stitches images together. Thelidar projection module 330 may also distinguish intensities of thepoint clouds in the lidar projection according to reflective intensitiesrecorded by the lidar.

The synchronization module 340 synchronizes the sensors on the vehicle150. In one embodiment, the synchronization module 340 synchronizes thelidar and the one or more cameras. As each sensor of the vehicle 150operates with individual clocks, the various clocks are subject tointroducing discrepancies amongst one another. To account for potentialtime drift in timestamps of data collected by the sensors, thesynchronization module 340 synchronizes the sensors. In particular, thesynchronization module 340 synchronizes the lidar and the one or morecameras such that the lidar projection properly aligns with the images.In addition, the synchronization module 340 may adjust the clocks in oneof the lidar and the cameras.

In a first method of synchronizing the lidar and one or more cameras,the synchronization module 340 uses time deltas to compute lidarprojections to model time drift between the lidar and the cameras. Thesynchronization module 340 iterates through a plurality of time deltasto adjust the camera timestamps. The synchronization module 340 thenprovides the adjusted camera timestamps with the lidar scan and theimages to the lidar projection module 330. The synchronization module340 receives, in return, lidar projections according to the adjustedcamera timestamps. The synchronization module 340 determines analignment score for each of the lidar projections based on alignment tothe images. In one embodiment, the synchronization module 340 calculatesan alignment edge score of a feature identified in the lidar projectionand the images, wherein the alignment score is dependent on thecalculated alignment edge score. In another embodiment, the alignmentscore is provided as input by a reviewer. Based on the alignment scores,the synchronization module 340 selects a time delta as a time shift forthe track sample. In some embodiments, the synchronization module 340selects multiple track samples along a route for modeling the time shiftalong the route. The first manner of synchronization will be furtherdescribed in conjunction with FIGS. 5-6.

In a second method of synchronizing the lidar and one or more cameras,the synchronization module 340 iterates through various start times forthe cameras to determine a synchronized start time between the camerasand the lidar. According to this second manner, the synchronizationmodule 340 identifies a reflective surface within a threshold distancefrom the lidar and the cameras and in fields of view of the cameras. Thesynchronization module 340 instructs the lidar to emit a plurality oflight columns against the reflective surface at a lidar frequency. Thesynchronization module 340 configures the cameras to detect the lightcolumns emitted by the lidar. The synchronization module 340 theninstructs start times for the cameras from a plurality of start times.The synchronization module 340 analyzes the image data received from thecameras to calculate an alignment score for each of the many starttimes. The start time with the optimal alignment score is selected andused for to synchronize the lidar and the cameras. The second manner ofsynchronization will be further described in conjunction with FIGS. 7,8A, 8B, and 8C.

The data store 350 stores track samples. The data store 350 may log arecord of historical track samples collected by sensors on the vehicle150. In some cases, the data store 350 receives synchronized tracksamples as synchronized by the synchronization module 340. The datastore 350 may additionally provide the synchronized track samples toother components of the vehicle computing system 120. The data store 350may additionally store lidar projections superimposed onto the images aspart of an origin track sample.

FIG. 4 illustrates a flowchart describing a process 400 of projecting alidar scan onto an image for use by the navigational system, accordingto an embodiment. In one or more embodiments, the process 400 may beimplemented by components of the perception module 210 of the vehiclecomputing system 120. In other embodiments, the HD map system 110executes the process 400. The process 400 may be executed during and/orafter navigation of the vehicle 150 on a route.

The perception module 210 obtains 410 a track sample comprising a lidarscan captured at a lidar timestamp, an image captured at a cameratimestamp, and consecutive poses inclusive of the duration of the tracksample. The lidar scan is captured by a single lidar with the lidartimestamp determined by an internal clock for the lidar. The image maybe a single image or a stitched image derived from multiple images. Theimage is tied to a camera timestamp determined by an internal clock forthe camera(s). The camera timestamp may be associated with a start timewhen capturing the image or a processing time when the captured imagedata is being stored by the camera. The consecutive poses bound theduration of the track sample. The consecutive poses may be determined bythe pose calculation module 310 using lidar data and/or image data todetermine a pose of the vehicle 150.

The perception module 210 projects 420 the lidar scan onto the image forthe track sample. In order to do so, the perception module 210 computes422 an unwinding transform with the consecutive processes with similarprinciples described under the lidar unwinding module 320. Theperception module 210 unwinds 424 the lidar scan according to theunwinding transform and the camera timestamp with similar principlesdescribed under the lidar unwinding module 320. Based on the unwoundlidar scan, the perception module 210 projects 426 the unwound lidarscan onto the image coordinates as a lidar projection with similarprinciples described under the lidar projection module 330. Theperception module 210 may additionally perform steps for synchronizingthe track sample such that the lidar projection aligns with the imagewith similar principles described under the synchronization module 340.The perception module 210 may then store the lidar projection with thetrack sample for downstream uses.

In some embodiments, the perception module 210 localizes 430 the vehicle150 with the lidar projection. The perception module 210 uses the lidarprojection in tandem with an HD map as stored in the local HD map store275 to determine a position of the vehicle in the HD map. In otherembodiments, the perception module 210 generates or updates 440 the HDmap with the lidar projection.

Vehicle Sensor Synchronization

FIG. 5 illustrates a flowchart describing a first method 500 forsynchronizing a lidar and a camera on a vehicle, according to anembodiment. The first method 500 may be implemented by the perceptionmodule 210 or more specifically the synchronization module 340.According to the first method 500, the perception module 210 maysynchronize the lidar and the camera along a route. The route may bespecifically chosen for the synchronization or may be a portion of areal-time route of the vehicle 150. In other embodiments, anycombination of modules of the HD map system 110 and modules of thevehicle computing system 120 may be used to accomplish the first method500.

The perception module 210 selects 510 a plurality of track samples [K]for the route. The perception module 210 may first select a route thatis substantially straight. The perception module 210 may consider posesof many track samples to determine whether the poses define a route thatwithin a tolerance of curvature. In addition, the perception module 210may select a route with the vehicle 150 moving above a threshold speed(e.g., 30 miles per hour (mph), 35 mph, 40 mph, 45 mph, 50 mph, 55 mph,and 60 mph). Along the selected route, the perception module 210 selectsthe plurality of track samples [K]. In some embodiments, the perceptionmodule 210 includes a first track sample near a start of the route(e.g., temporally or distance-wise) and a last track sample near an endof the route (e.g., temporally or distance-wise). The perception module210 may also select additional track samples that are evenly distributedbetween the first and the last track sample.

In additional embodiments, the perception module 210 selects 510 tracksamples with specific features identifiable in the lidar scan and/or theimages. In one or more embodiments, the perception module 210 parsesthrough the track samples and identifies features present in either thelidar scan or the images. In one example, the perception module 210identifies highly reflective features in the lidar scan for tracksamples, e.g., a reflector on a road. The perception module 210 mayselect such track samples for use in the method 500. In anotherembodiment, the perception module 210 identifies features substantiallyflat and perpendicular to a line of sight to the lidar and/or thecamera, e.g., a road sign.

For each track sample, the perception module 210 calculates 520 a timeshift. In order to calculate a time shift for each sample, theperception module 210 determines a set of time deltas [T] by which toadjust the camera timestamp. The time deltas may be on the order ofmilliseconds (ms), e.g., 0 ms, 1 ms, 2 ms, 3 ms, 4 ms, 5 ms, 6 ms, 7 ms,8 ms, 9 ms, 10 ms, 15 ms, 20 ms, 25 ms, 30 ms, etc. Additionally, thetime deltas can be positive or negative values effectively adding to thecamera timestamp (a later timestamp) or subtracting from the cameratimestamp (an earlier timestamp).

To calculate a time shift for a single track sample, the perceptionmodule 210 analyzes alignments of the image and lidar projections withadjusted camera timestamps. For each time delta [t] of the time deltas[T], the perception module 210 adjusts the camera timestamp by the timedelta. The perception module 210 projects 420 the lidar scan onto theimage coordinates according to the adjusted camera timestamp. Theprojection step 420 implements similar principles as described above inFIG. 4. In one embodiment, the perception module 210 unwinds the lidarscan and projects the unwound lidar scan onto the image coordinatesusing a lidar-camera transform with inputs of the lidar scan and theadjusted camera timestamp. With the lidar projection at each adjustedcamera timestamp and the image, the perception module 210 calculates 540an alignment score for that lidar projection. Calculation of thealignment score can be accomplished in two manners which will be furtherdescribed below. Once an alignment score is calculated for a first timedelta [t0] in the plurality of time deltas [T], the perception module210 iterates through the remaining time deltas to calculate an alignmentscore for each of the time deltas.

A first embodiment obtains input that is used in calculating thealignment score. The perception module 210 superimposes the lidarprojection onto the image. Additionally, the perception module 210visually distinguishes the projected point clouds according to areflective intensity value as recorded by the lidar. For example, theperception module 210 uses a color spectrum to distinguish point cloudsof varying reflective intensity values. For example, a point cloudcolored red will be of a higher reflective intensity value compared to apoint cloud colored purple of a lower reflective intensity value. Theperception module 210 provides the lidar projections superimposed ontothe image to a user for manual review. The perception module 210generates a graphical user interface (GUI) for displaying the lidarprojections superimposed onto the image. For example, the GUI mayreceive input to switch between each of the lidar projections for atrack sample. A user of the GUI may view each of the lidar projectionsand provide inputs which may be used to calculate the alignment scorefor each of the lidar projections. In some embodiments, the vehiclecomputing system 120 has an electronic display for displaying the GUI.In these embodiments, the perception module 210 presents the GUI to theelectronic display for review by a user in the vehicle 150. The user mayprovide an input for each lidar projection. The perception module 210may receive each input corresponding to an alignment score. For example,the GUI also presents a slider for allowing the user to provide inputthrough the slider. One side of the slider may correspond to maximalalignment of the lidar projection with the opposite side correspondingto minimal alignment. In other embodiments, the HD map system 110 has anelectronic display or is connected to an external system with anelectronic display. In these embodiments, the HD map system 110 receivesthe GUI from the perception module 210 and also receives the input thatis transmitted to the perception module 210.

Another embodiment calculates alignment scores for lidar projectionsbased on identifiable features. The perception module 210 identifies oneor more informative features in both the lidar projection and the image.The perception module 210 identifies an informative feature in the lidarprojection by first determining point clouds in a ground plane of thelidar projection. The perception module 210 may fit a ground plane inthe lidar projection using Random Sample Consensus (RANSAC). Within thepoint clouds in the fitted ground plane, the perception module 210identifies highly reflective features based on reflective intensityvalues of the point clouds. Reflectors on the road are highly reflectiveobjects that would provide large reflective intensity values in thepoint clouds. The perception module 210 may group point clouds with highreflective intensity values in close proximity to one another as theidentified informative feature. In addition, the perception module 210calculates a lidar edge score based on a differential of a lidar pointcloud's reflective intensity value compared to reflective intensityvalues of adjacent point clouds in a lidar scan line. Lidar point cloudswith a higher lidar edge score would correspond to lidar point cloudscloser to edges of features detected in the lidar scan.

Additionally, the perception module 210 identifies the same informativefeature from the lidar projection in the image. The perception module210 may convert the pixels in the image to intensity values (e.g., ingrayscale). The perception module 210 then calculates an image edgescore for each pixel as a gradient strength by taking a differential ofthat pixel's intensity value with intensity values of neighboringpixels. The gradient strength of the pixels may be used by theperception module 210 to outline features in the image. The perceptionmodule 210 may further identify portions of the image, e.g., a firstportion relating to the road and a second portion relating to the sky.The perception module 210 then identifies pixels of the informativefeature. One way to do so involves searching for features in proximityto the identified feature in the lidar projection. Another way to do soinvolves searching in portions of the image that would correspond to thedesired feature, e.g., looking to the first portion relating to the roadto identify pixels relating to a reflector. With the identified pixelsfor a feature, the perception module 210 defines a shape of theinformative feature in the image. The perception module 210, in asimilar manner to identifying the informative feature in the lidarprojection, may determine whether the shape of the informative featureis approximate to a selected shape. For example, the perception module210 searches for identified features that fit a rectangular shape. Theperception module 210 may, consequently, disregard features that do notfit the shape.

Once the informative feature is identified in the lidar projection andthe image, the perception module 210 may calculate an alignment scorefor the lidar projection. The perception module 210 superimposes theinformative feature in the lidar projection onto the image. Since thelidar projection and the image use the image coordinates, the perceptionmodule 210 can compare the alignment of the lidar projection and theimage. The perception module 210 calculates an alignment edge score bycomparing the lidar edge scores of the point clouds associated with theinformative feature in the lidar projection to the image edge scores ofthe pixels associated with the same informative feature identified inthe image. The perception module 210 calculates, for each imagecoordinate, an edge score as a product of the lidar edge score of thepoint cloud in the lidar projection and the image edge score of thepixel in the image. The perception module 210 may then define thealignment score of the lidar projection according to the alignment edgescores. In one embodiment, the perception module 210 defines thealignment score as a sum of the alignment edge scores. In some examples,a higher alignment score, therefore, refers to better alignment betweenthe lidar projection and the image compared to a lower alignment score.

The perception module 210 selects 550 a time delta with the highestalignment score as the time shift for the track sample. Once all timedeltas are iterated through producing an alignment score of the lidarprojection compared to the image, the perception module 210 selects 550the time delta with the highest alignment score as the time shift forthe track sample, an example of this selection will be discussed inconjunction with FIG. 6. Once the perception module 210 calculates atime shift for a first track sample [k0], the perception module 210iterates through the remaining track samples to calculate a time shiftfor each of the track samples [K].

With the calculated time shifts of the track samples, the perceptionmodule 210 models 560 the time drift between the lidar and the cameraover the route. The perception module 210 may model 560 the time driftby plotting the time shift of each track sample over the plurality oftrack samples [K] selected in the route. In one embodiment, theperception module 210 may connect adjacent time shifts providinginterpolating ability. In other embodiments, perception module 210 mayrather linearly regress the time shifts. In alternative embodiments, theperception module 210 may rather regress the time shifts according toother techniques—e.g., polynomial regressions, logistic regressions,other regression algorithms, etc.

The perception module 210 synchronizes 570 the lidar and the camera overthe route based on the modeled time drift. The perception module 210adjusts either the lidar timestamp or the camera timestamp for each ofthe track samples [K] based on the calculated time shift. For othertrack samples not selected in step 510, the perception module 210predicts a time shift according to the modeled time drift. With tracksamples between the first track sample and the last track sampleselected, the perception module 210 may use the modeled time drift tointerpolate a predicted time shift. With track samples outside of thefirst and the last track sample, the perception module 210 may use themodeled time drift to extrapolate a predicted time shift. Theinterpolation/extrapolation can be linear or non-linear (e.g., byfitting a polynomial). In addition, the perception module 210 may adjusteither the lidar internal clock or the camera internal clock tosynchronize the two clocks for subsequent collection of lidar scans andimages. According to this first method of synchronizing the lidar andthe camera, the perception module 210 is able to achieve a precision onthe order of milliseconds, e.g., 1 ms, 2 ms, 3 ms, 4 ms, 5 ms, 6 ms, 7ms, 8 ms, 9 ms, or 10 ms.

FIG. 6 demonstrates the first method for synchronizing the lidar and thecamera, according to an embodiment. The perception module 210accomplishes the first method 500 for synchronizing the lidar and thecamera. In this illustrative example, the perception module 210 iscalculating 520 a time shift for a track sample. The track sample 610includes the image 610 with an identified informative feature—i.e. theroad reflector 620. In order to calculate the time shift, the perceptionmodule 210 calculates an alignment score for a plurality of lidarprojections In this example, the time deltas used for adjustment of thecamera timestamp include −15 ms, −10 ms, −5 ms, 0 ms, +5 ms, +10 ms, and+15 ms.

With the identified informative feature in both the lidar projection andthe image, the perception module 210 calculates an alignment score foreach time delta. The lidar projection is superimposed onto the image assuperimpositions 630, 640, 650, 660, 670, 680, and 690 corresponding totime deltas −15 ms, −10 ms, −5 ms, 0 ms, +5 ms, +10 ms, and +15 ms,respectively.

According to the first manner for calculating an alignment score, thesuperimpositions are provided to a user through a graphical userinterface. In this first manner, the superimpositions may include notjust the identified feature but the entire lidar projection superimposedonto the image. The user may provide input for each of thesuperimpositions or may simply select the superimposition with theclosest alignment. In this illustrative example, the user may review theset of superimpositions and may end up selecting time delta +5 ms as theone with optimal alignment. The perception module 210 receives the inputand may determine the time shift for the track sample to be +5 ms.

According to the second manner for calculating an alignment score, thesuperimpositions are scored by the perception module 210. The perceptionmodule 210 identifies the informative feature in both the lidarprojection and the image. Once identified, the perception module 210 mayfurther determine a lidar outline of the informative feature in thelidar projection and an image outline in the image. The perceptionmodule 210 calculates an edge score according to the outlines. Theperception module 210 then calculates the alignment score according tothe calculated edge scores. In this example, the perception module 210may calculate an alignment score out of 100 for each of thesuperimpositions. Noticeably, superimpositions 630, 640, 650, and 690are quite off. Superimpositions 660 and 680 are closer yet, butsuperimposition 670 has the optimal alignment. In superimposition 670,the perception module 210 may calculate an edge score that is the lowestamongst the set. The superimposition 670 has overall a lowest distancebetween paired point clouds and image pixels compared to others in theset.

FIG. 7 illustrates a flowchart describing a second method 700 forsynchronizing a lidar and a camera on a vehicle, according to anembodiment. The second method 700 may be implemented by the perceptionmodule 210 or more specifically the synchronization module 340.According to the second method 700, the perception module 210 maysynchronize the lidar and the camera at a stopped position along aroute. The second method 700 relies on using the camera to detecttransmitted light columns from the lidar.

Reference now is made to FIG. 8A; FIG. 8A illustrates a placementconfiguration of the lidar and the camera for the second method 700 forsynchronizing the lidar and the camera, according to an embodiment. Forthe second method 700, a reflective surface 830 is placed in a field ofview of a lidar 810 and a camera 820. The reflective surface issubstantially reflective such that transmitted light columns 840 on thereflective surface 830 may be detected and imaged by the camera 820. Forthis second method 700, the camera is configured to detect wavelengthsof light emitted by the lidar, e.g. infrared light. In one or moreembodiments, the perception module 210 analyzes an image from the camerato determine whether the reflective surface 830 is substantially wide tobe incident with some number of light columns, e.g., 2, 3, 4, 5, 6, 7,8, 9, or 10 light columns. In additional embodiments, the perceptionmodule 210 analyzes a lidar scan to determine whether the reflectivesurface 830 is within a threshold distance from the lidar 810 and thecamera 820, e.g., within 1, 2, 3, 4, or 5 meters. In one example, aviable reflective surface for use in this second method 700 is a whitematte board that is wide enough to at least capture 3 light columnstransmitted by the lidar 810.

Now referring back to the second method 700, the perception module 210instructs the lidar to transmit 710 a plurality of light columns at alidar frequency within a field of view of the camera onto a reflectivesurface. The lidar frequency describes a frequency at which the lidartransmits light columns for a lidar scan, e.g., 10 Hz.

The perception module 210 then determines a plurality of start times [T]and instructs the camera to capture 720 a plurality of images [Z] at acamera frequency from each of the start times [T]. Each of the images iscaptured over an exposure time, e.g., 5 ms or at a shutter speed of1/200 seconds. The perception module 210 may determine the plurality ofstart times [T] based on the lidar frequency, the camera frequency, andthe camera exposure time. In one embodiment, the perception module 210determines to use some number of start times based on a ratio of thecamera exposure time and the camera frequency. For example, the camerafrequency may be 30 Hz with the exposure time being 5 ms (200 Hz) suchthat the ratio would be ˜6.67. Accordingly, the perception module 210determines that at least 7 start times are needed that are 5 ms apart: 0ms, 5 ms, 10 ms, 15 ms, 20 ms, 25 ms, and 30 ms. In another embodiment,the perception module 210 may determine start times with some overlap ofexposure time, e.g., 0 ms, 4 ms, 8 ms, etc. In some embodiments, thenumber of images [Z] captured by the camera for each start time isdetermined by a ratio of the camera frequency and the lidar frequency.For example, the camera frequency is 30 Hz and the lidar frequency is 10Hz such that the ratio would be 3. Accordingly, the perception module210 instructs the camera to capture 3 images for each start time.

Once the images are captured for a start time, the perception module 210identifies 730 one or more light columns in each image. The perceptionmodule 210 identifies 210 the light columns through the followingprocess. In some embodiments, the camera is instructed by the perceptionmodule 210 to apply an infrared filter. After having applied theinfrared filter, the perception module 210 transforms each pixel in theimage to an intensity value (e.g., in grayscale). The intensity value ofa pixel corresponds directly to an amount of infrared light detected bythe camera at that pixel. The perception module 210 calculates agradient strength for each pixel based on the intensity value of thatpixel and its neighboring pixels. Based on the calculated gradientstrengths, the perception module 210 identifies pixels above a thresholdgradient strength. The perception module 210 may further group pixelsoutlining light columns captured by the image from which to count anumber of columns in the image.

The perception module 210 calculates 740 an alignment score for eachstart time based on the identified columns in the images. The perceptionmodule 210 counts the number of identified columns in the images for astart time. The perception module 210 computes the alignment scoreaccording to the count. For example, for a start time with three images,there is only light column visible in only one of the images with theother images having no light columns. The perception module 210 maydetermine the count to be the alignment score or may average the countto be the alignment score.

The perception module 210 selects 750 a start time with the highestalignment score. The perception module 210 may rank the start timesbased on the alignments scores and based on the ranking select thehighest start time with the highest alignment score. In additionalembodiments, the perception module 210 may further select the image inthe start time that had the highest number of identified light columns.

The perception module 210 synchronizes 760 the lidar and the camerabased on the selected start time. In one or more embodiments, theperception module 210 adjusts either the lidar clock or the camera clockaccording to the selected start time. For example, the perception module210 adjusts the camera clock to start the camera at the selected starttime. In some embodiments, the perception module 210 rather adjustseither the lidar clock or the camera clock according to the selectedimage in the selected start time. For example, a start time of 8 ms witheach subsequent image captured at 30 Hz has the second image having themost number of identified columns. The perception module may determineto adjust the camera clock to start capturing images at 41 ms which is asum of 8 ms start time with the second image starting 33.33 ms after thefirst image. The precision of the second method 700 is dependent on howthe camera can be aligned to the lidar frequency—e.g., 5 Hz, 10 Hz, or20 Hz. The precision may also be dependent on start time precision ofthe camera—e.g., 30 microseconds (μB). Due to this, the second method700 may have a synchronization precision on the order of tens ofmicroseconds, e.g., 20 μs, 25 μs, 30 μs, 35 μs, 40 μs, 45 μs, 50 μs, 60μs, 70 μs, 80 μs, 90 μs, 100 μs, etc. For example, if the lidarfrequency is 10 Hz—i.e. every 100 ms the lidar emits light columns onthe reflective surface, the camera's precision of triggering start timesas 30 μs would result in a synchronization precision of 30 μs.

FIGS. 8B & 8C demonstrates the second method 700 for synchronizing thelidar 810 and the camera 820, according to an embodiment. The lidar 810and the camera 820 are placed in according to the placementconfiguration shown in FIG. 8A. FIG. 8B shows a timeline demonstratingthe second method 700, and FIG. 8C illustrates example images of lightcolumns captured by the camera 820. Discussion of FIGS. 8B & 8C willsimultaneously reference both figures.

FIG. 8B illustrates two timelines for two start times 850 and 860 forcapturing images by the camera 820. The top of each timeline shows ablock of time—a lidar incident time 870—when the light columns 840transmitted by the lidar 810 is incident on the reflective surface 830.The lidar 810 transmits at a lidar frequency 875 which is denoted by atemporal distance between the moments each lidar incident time 870begins to transmit light columns 840 onto the reflective surface 830. Acamera exposure 880 is a duration of time when the camera 820 iscapturing an image—may also be referred to as the shutter speed of thecamera 820. A camera frequency 885 is a frequency at which the camera820 is capturing images. The goal of synchronizing the lidar 810 and thecamera 820 may be visually represented by seeking to temporally coincidethe lidar incident time 870 and the camera exposure 880. In FIG. 8C, theimage that captures the light columns 840 incident on the reflectivesurface 830 within the exposure time 880 displays all of the three lightcolumns in light columns 840. Between a first start time 850 and asecond start time 860 is a start time shift 890.

In the first timeline for start time 850, the camera 820 begins tocapture images at 0 ms. The first captured image for the start time 850of 0 ms and the third image does not capture any of the light columns840. As shown in FIG. 8C, the first image of the start time 850 at 0 msand the third image at 67 ms have no light columns captured. In FIG. 8B,there is a partial overlap between the camera exposure 880 and the lidarincident time 870 in the start time 850 at the second captured image. InFIG. 8C, the second image of the start time 850 at 33 ms captures afirst column of the light columns 840. The perception module 210 mayscore the start time 850 according to the one identified one lightcolumn in the second image, e.g., an alignment score of 1.

In the second timeline for start time 860, the camera 820 begins tocapture images at 4 ms wherein the start time shift 890 is 4 ms betweenstart time 850 and start time 860. Similar to start time 850, a firstand a third image for start time 860 do not temporally coincide with thelidar incident time 870. As seen in FIG. 8C, the first image at 4 ms andthe third image at 71 ms do not show any captured light columns.However, a second image for start time 860 has an exposure time 880 thatcompletely overlaps with the lidar incident time 870. As seen in FIG.8C, the second image at 37 ms has captured all three light columns inthe light columns 840. The perception module 210 may score the starttime 860 according to the three identified light columns in the secondimage, e.g., an alignment score of 3.

The perception module 210 selects 750 start time 860 with the highestalignment score—i.e. between start time 850 and start time 860. Theperception module 210 adjusts the camera to start at 37 ms such thatthere is complete overlap between the camera exposure time 880 and thelidar incident time 870.

HD Map System Architecture

FIG. 10 shows the system architecture of an HD map system, according toan embodiment. The HD map system 110 comprises a map creation module1010, a map update module 1020, a map data encoding module 1030, a loadbalancing module 1040, a map accuracy management module, a vehicleinterface module, and a HD map store 165. Other embodiments of HD mapsystem 110 may include more or fewer modules than shown in FIG. 10.Functionality indicated as being performed by a particular module may beimplemented by other modules. In an embodiment, the HD map system 110may be a distributed system comprising a plurality of processors.

The map creation module 1010 creates the map from map data collectedfrom several vehicles that are driving along various routes. The mapupdate module 1020 updates previously computed map data by receivingmore recent information from vehicles that recently traveled alongroutes on which map information changed. For example, if certain roadsigns have changed or lane information has changed as a result ofconstruction in a region, the map update module 1020 updates the mapsaccordingly. The map data encoding module 1030 encodes map data to beable to store the data efficiently as well as send the required map datato vehicles 150 efficiently. The load balancing module 1040 balancesload across vehicles to ensure that requests to receive data fromvehicles are uniformly distributed across different vehicles. The mapaccuracy management module 1050 maintains high accuracy of the map datausing various techniques even though the information received fromindividual vehicles may not have high accuracy.

High Definition (HD) Map

FIG. 11 illustrates the components of a high definition (HD) map,according to an embodiment. The HD map comprises maps of severalgeographical regions. The HD map 1110 of a geographical region comprisesa landmark map (LMap) 1120 and an occupancy map (OMap) 1130. Thelandmark map comprises information describing lanes including spatiallocation of lanes and semantic information about each lane. The spatiallocation of a lane comprises the geometric location in latitude,longitude and elevation at high prevision, for example, at or below 10cm precision. The semantic information of a lane comprises restrictionssuch as direction, speed, type of lane (for example, a lane for goingstraight, a left turn lane, a right turn lane, an exit lane, and thelike), restriction on crossing to the left, connectivity to other lanesand so on. The landmark map may further comprise information describingstop lines, yield lines, spatial location of cross walks, safelynavigable space, spatial location of speed bumps, curb, and road signscomprising spatial location and type of all signage that is relevant todriving restrictions. Examples of road signs described in an HD mapinclude stop signs, traffic lights, speed limits, one-way, do-not-enter,yield (vehicle, pedestrian, animal), and so on.

The occupancy map 1130 comprises spatial 3-dimensional (3D)representation of the road and all physical objects around the road. Thedata stored in an occupancy map 1130 is also referred to herein asoccupancy grid data. The 3D representation may be associated with aconfidence score indicative of a likelihood of the object existing atthe location. The occupancy map 1130 may be represented in a number ofother ways. In one embodiment, the occupancy map 1130 is represented asa 3D mesh geometry (collection of triangles) which covers the surfaces.In another embodiment, the occupancy map 1130 is represented as acollection of 3D points which cover the surfaces. In another embodiment,the occupancy map 1130 is represented using a 3D volumetric grid ofcells at 5-10 cm resolution. Each cell indicates whether or not asurface exists at that cell, and if the surface exists, a directionalong which the surface is oriented.

The occupancy map 1130 may take a large amount of storage space comparedto a landmark map 1120. For example, data of 1 GB/Mile may be used by anoccupancy map 1130, resulting in the map of the United States (including4 million miles of road) occupying 4×1015 bytes or 4 petabytes.Therefore the HD map system 110 and the vehicle computing system 120 usedata compression techniques for being able to store and transfer mapdata thereby reducing storage and transmission costs. Accordingly, thetechniques disclosed herein make self-driving of autonomous vehiclespossible.

In one embodiment, the HD Map does not require or rely on data typicallyincluded in maps, such as addresses, road names, ability to geo-code anaddress, and ability to compute routes between place names or addresses.The vehicle computing system 120 or the HD map system 110 accesses othermap systems, for example, GOOGLE MAPs to obtain this information.Accordingly, a vehicle computing system 120 or the HD map system 110receives navigation instructions from a tool such as GOOGLE MAPs into aroute and converts the information to a route based on the HD mapinformation.

Geographical Regions in HD Maps

The HD map system 110 divides a large physical area into geographicalregions and stores a representation of each geographical region. Eachgeographical region represents a contiguous area bounded by a geometricshape, for example, a rectangle or square. In an embodiment, the HD mapsystem 110 divides a physical area into geographical regions of the samesize independent of the amount of data required to store therepresentation of each geographical region. In another embodiment, theHD map system 110 divides a physical area into geographical regions ofdifferent sizes, where the size of each geographical region isdetermined based on the amount of information needed for representingthe geographical region. For example, a geographical region representinga densely populated area with a large number of streets represents asmaller physical area compared to a geographical region representingsparsely populated area with very few streets. Accordingly, in thisembodiment, the HD map system 110 determines the size of a geographicalregion based on an estimate of an amount of information required tostore the various elements of the physical area relevant for an HD map.

In an embodiment, the HD map system 110 represents a geographic regionusing an object or a data record that comprises various attributesincluding, a unique identifier for the geographical region, a uniquename for the geographical region, description of the boundary of thegeographical region, for example, using a bounding box of latitude andlongitude coordinates, and a collection of landmark features andoccupancy grid data.

FIGS. 12A-B illustrate geographical regions defined in an HD map,according to an embodiment. FIG. 12A shows a square geographical region1210 a. FIG. 12B shows two neighboring geographical regions 1210 a and1210 b. The HD map system 110 stores data in a representation of ageographical region that allows for smooth transition from onegeographical region to another as a vehicle drives across geographicalregion boundaries.

According to an embodiment, as illustrated in FIG. 12, each geographicregion has a buffer of a predetermined width around it. The buffercomprises redundant map data around all 4 sides of a geographic region(in the case that the geographic region is bounded by a rectangle). FIG.12A shows a boundary 1220 for a buffer of 50 meters around thegeographic region 1210 a and a boundary 1230 for buffer of 100 metersaround the geographic region 1210 a. The vehicle computing system 120switches the current geographical region of a vehicle from onegeographical region to the neighboring geographical region when thevehicle crosses a threshold distance within this buffer. For example, asshown in FIG. 12B, a vehicle starts at location 1250 a in thegeographical region 1210 a. The vehicle traverses along a route to reacha location 1250 b where it cross the boundary of the geographical region1210 but stays within the boundary 1220 of the buffer. Accordingly, thevehicle computing system 120 continues to use the geographical region1210 a as the current geographical region of the vehicle. Once thevehicle crosses the boundary 1220 of the buffer at location 1250 c, thevehicle computing system 120 switches the current geographical region ofthe vehicle to geographical region 1210 b from 1210 a. The use of abuffer prevents rapid switching of the current geographical region of avehicle as a result of the vehicle travelling along a route that closelytracks a boundary of a geographical region.

Lane Representations in HD Maps

The HD map system 110 represents lane information of streets in HD maps.Although the embodiments described herein refer to streets, thetechniques are applicable to highways, alleys, avenues, boulevards, orany other path on which vehicles can travel. The HD map system 110 useslanes as a reference frame for purposes of routing and for localizationof a vehicle. The lanes represented by the HD map system 110 includelanes that are explicitly marked, for example, white and yellow stripedlanes, lanes that are implicit, for example, on a country road with nolines or curbs but two directions of travel, and implicit paths that actas lanes, for example, the path that a turning car makes when entering alane from another lane. The HD map system 110 also stores informationrelative to lanes, for example, landmark features such as road signs andtraffic lights relative to the lanes, occupancy grids relative to thelanes for obstacle detection, and navigable spaces relative to the lanesso the vehicle can efficiently plan/react in emergencies when thevehicle must make an unplanned move out of the lane. Accordingly, the HDmap system 110 stores a representation of a network of lanes to allow avehicle to plan a legal path between a source and a destination and toadd a frame of reference for real time sensing and control of thevehicle. The HD map system 110 stores information and provides APIs thatallow a vehicle to determine the lane that the vehicle is currently in,the precise vehicle location relative to the lane geometry, and allrelevant features/data relative to the lane and adjoining and connectedlanes.

FIG. 13 illustrates lane representations in an HD map, according to anembodiment. FIG. 13 shows a vehicle 1310 at a traffic intersection. TheHD map system provides the vehicle with access to the map data that isrelevant for autonomous driving of the vehicle. This includes, forexample, features 1320 a and 1320 b that are associated with the lanebut may not be the closest features to the vehicle. Therefore, the HDmap system 110 stores a lane-centric representation of data thatrepresents the relationship of the lane to the feature so that thevehicle can efficiently extract the features given a lane.

The HD map system 110 represents portions of the lanes as lane elements.A lane element specifies the boundaries of the lane and variousconstraints including the legal direction in which a vehicle can travelwithin the lane element, the speed with which the vehicle can drivewithin the lane element, whether the lane element is for left turn only,or right turn only, and so on. The HD map system 110 represents a laneelement as a continuous geometric portion of a single vehicle lane. TheHD map system 110 stores objects or data structures representing laneelements that comprise information representing geometric boundaries ofthe lanes; driving direction along the lane; vehicle restriction fordriving in the lane, for example, speed limit, relationships withconnecting lanes including incoming and outgoing lanes; a terminationrestriction, for example, whether the lane ends at a stop line, a yieldsign, or a speed bump; and relationships with road features that arerelevant for autonomous driving, for example, traffic light locations,road sign locations and so on.

Examples of lane elements represented by the HD map system 110 include,a piece of a right lane on a freeway, a piece of a lane on a road, aleft turn lane, the turn from a left turn lane into another lane, amerge lane from an on-ramp an exit lane on an off-ramp, and a driveway.The HD map system 110 represents a one lane road using two laneelements, one for each direction. The HD map system 110 representsmedian turn lanes that are shared similar to a one-lane road.

FIGS. 14A-B illustrates lane elements and relations between laneelements in an HD map, according to an embodiment. FIG. 14A shows anexample of a T junction in a road illustrating a lane element 1410 athat is connected to lane element 1410 c via a turn lane 1410 b and isconnected to lane 1410 e via a turn lane 1410 d. FIG. 14B shows anexample of a Y junction in a road showing label 1410 f connected to lane1410 h directly and connected to lane 1410 i via lane 1410 g. The HD mapsystem 110 determines a route from a source location to a destinationlocation as a sequence of connected lane elements that can be traversedto reach from the source location to the destination location.

What is claimed is:
 1. A method for synchronizing a light detection andranging sensor (lidar) and a camera on an autonomous vehicle,comprising: transmitting, via the lidar, a plurality of light columns ata lidar frequency within a field of view (FOV) of the camera; for eachstart time of a plurality of start times: capturing, via the camera, aplurality of images at a camera frequency, each image captured over anexposure time, for each image of the plurality of images, identifying anumber of light columns in the image, and calculating an alignment scorebased on the identified number of light columns in each of the pluralityof images; selecting a start time with the highest alignment score;synchronizing the lidar and the camera according to the selected starttime; and navigating the autonomous vehicle with lidar data detected bythe synchronized lidar and image data captured by the synchronizedcamera.
 2. The method of claim 1, wherein the plurality of light columnscomprises at least three light columns within the FOV of the camera. 3.The method of claim 1, wherein the lidar emits the light columns in aninfrared spectrum, and wherein the camera detects light in at least theinfrared spectrum.
 4. The method of claim 1, wherein for each starttime, for each image, identifying the number of light columns in theimage, further comprises: transforming each pixel in the image into anintensity value; calculating a gradient strength for each pixel based onintensity values of neighboring pixels; and identifying one or morepixels above a threshold gradient strength; wherein the number of lightcolumns in the image is according to the identified one or more pixels.5. The method of claim 1, wherein the lidar emits the light columnstowards a reflective surface within a threshold distance from the lidarand the camera.
 6. The method of claim 5, wherein the reflective surfaceis sufficiently wide to be incident with a threshold number of lightcolumns.
 7. The method of claim 1, wherein the plurality of start timesis determined based on the exposure time for the camera and the camerafrequency.
 8. The method of claim 7, wherein a number of start times forthe plurality of start times is based on a ratio of the exposure timefor the camera to the camera frequency.
 9. The method of claim 7,wherein a first start time and a second start time of the plurality ofstart times are within the exposure time for the camera.
 10. The methodof claim 1, wherein a number of images for the plurality of images isdetermined based on the camera frequency and the lidar frequency. 11.The method of claim 1, wherein for each start time, calculating thealignment score based on the determination in each of the plurality ofimages, further comprises: counting a sum of light columns determined tobe present in the plurality of images; wherein the alignment score isbased on the sum of light columns.
 12. The method of claim 1, whereinsynchronizing the lidar and the camera comprises adjusting one or moreof: a lidar clock on the lidar and a camera clock on the camera.
 13. Anon-transitory computer-readable storage medium for synchronizing alight detection and ranging sensor (lidar) and a camera on an autonomousvehicle, the non-transitory computer-readable storage medium storinginstructions that, when executed by a processor, cause the processor toperform operations comprising: transmitting, via the lidar, a pluralityof light columns at a lidar frequency within a field of view (FOV) ofthe camera; for each start time of a plurality of start times:capturing, via the camera, a plurality of images at a camera frequency,each image captured over an exposure time, for each image of theplurality of images, identifying a number of light columns in the image,and calculating an alignment score based on the identified number oflight columns in each of the plurality of images; selecting a start timewith the highest alignment score; synchronizing the lidar and the cameraaccording to the selected start time; and navigating the autonomousvehicle with lidar data detected by the synchronized lidar and imagedata captured by the synchronized camera.
 14. The non-transitorycomputer-readable storage medium of claim 13, wherein the plurality oflight columns comprises at least three light columns within the FOV ofthe camera.
 15. The non-transitory computer-readable storage medium ofclaim 13, wherein the lidar emits the light columns in an infraredspectrum, and wherein the camera detects light in at least the infraredspectrum.
 16. The non-transitory computer-readable storage medium ofclaim 13, wherein for each start time, for each image, identifying thenumber of light columns in the image, further comprises: transformingeach pixel in the image into an intensity value; calculating a gradientstrength for each pixel based on intensity values of neighboring pixels;and identifying one or more pixels above a threshold gradient strength;wherein the number of light columns in the image is according to theidentified one or more pixels.
 17. The non-transitory computer-readablestorage medium of claim 13, wherein the lidar emits the light columnstowards a reflective surface within a threshold distance from the lidarand the camera.
 18. The non-transitory computer-readable storage mediumof claim 17, wherein the reflective surface is sufficiently wide to beincident with a threshold number of light columns.
 19. Thenon-transitory computer-readable storage medium of claim 13, wherein theplurality of start times is determined based on the exposure time forthe camera and the camera frequency.
 20. The non-transitorycomputer-readable storage medium of claim 19, wherein a number of starttimes for the plurality of start times is based on a ratio of theexposure time for the camera to the camera frequency.
 21. Thenon-transitory computer-readable storage medium of claim 19, wherein afirst start time and a second start time of the plurality of start timesare within the exposure time for the camera.
 22. The non-transitorycomputer-readable storage medium of claim 13, wherein a number of imagesfor the plurality of images is determined based on the camera frequencyand the lidar frequency.
 23. The non-transitory computer-readablestorage medium of claim 13, wherein for each start time, calculating thealignment score based on the determination in each of the plurality ofimages, further comprises: counting a sum of light columns determined tobe present in the plurality of images; wherein the alignment score isbased on the sum of light columns.
 24. The non-transitorycomputer-readable storage medium of claim 13, wherein synchronizing thelidar and the camera comprises adjusting one or more of: a lidar clockon the lidar and a camera clock on the camera.
 25. A system comprising:a lidar on an autonomous vehicle; a camera on the autonomous vehicle; aprocessor; and a non-transitory computer-readable storage medium forsynchronizing a light detection and ranging sensor (lidar) and a cameraon an autonomous vehicle, the non-transitory computer-readable storagemedium storing instructions that, when executed by the processor, causethe processor to perform operations comprising: transmitting, via thelidar, a plurality of light columns at a lidar frequency within a fieldof view (FOV) of the camera; for each start time of a plurality of starttimes: capturing, via the camera, a plurality of images at a camerafrequency, each image captured over an exposure time, for each image ofthe plurality of images, identifying a number of light columns in theimage, and calculating an alignment score based on the identified numberof light columns in each of the plurality of images; selecting a starttime with the highest alignment score; synchronizing the lidar and thecamera according to the selected start time; and navigating theautonomous vehicle with lidar data detected by the synchronized lidarand image data captured by the synchronized camera.
 26. The system ofclaim 25, wherein the plurality of light columns comprises at leastthree light columns within the FOV of the camera.
 27. The system ofclaim 25, wherein the lidar emits the light columns in an infraredspectrum, and wherein the camera detects light in at least the infraredspectrum.
 28. The system of claim 25, wherein for each start time, foreach image, identifying the number of light columns in the image,further comprises: transforming each pixel in the image into anintensity value; calculating a gradient strength for each pixel based onintensity values of neighboring pixels; and identifying one or morepixels above a threshold gradient strength; wherein the number of lightcolumns in the image is according to the identified one or more pixels.29. The system of claim 25, wherein for each start time, calculating thealignment score based on the determination in each of the plurality ofimages, further comprises: counting a sum of light columns determined tobe present in the plurality of images; wherein the alignment score isbased on the sum of light columns.
 30. The system of claim 25, whereinsynchronizing the lidar and the camera comprises adjusting one or moreof: a lidar clock on the lidar and a camera clock on the camera.